Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Abdollahi, M.; Zhu, Y.; Guimãraes, H. R.; Coallier, N.; Maucourt, S.; Giovenazzo, P.; Falk, T. H.
On the Prediction of Varroa Mite Infestations in Honey Bee Colonies via Acoustic Monitoring Article de journal
Dans: IEEE Sensors Journal, vol. 26, no 8, p. 12157–12167, 2026, ISSN: 1530437X (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Acoustic measuring instruments, Acoustic monitoring, Acoustic variables measurement, Acoustics, Beehive acoustic, Beehive acoustics, Ecosystems, Food products, Honey bee, honey bees, Honeybee colonies, Learning systems, Machine learning approaches, Modulation spectrogram, Motion compensation, Nearest neighbor search, Parasite-, Random forests, Sanitary sewers, Spectrograms, Spectrographs, Support vector machines, Varroa destructor, Varroa mite infestation
@article{abdollahiPredictionVarroaMite2026,
title = {On the Prediction of Varroa Mite Infestations in Honey Bee Colonies via Acoustic Monitoring},
author = {M. Abdollahi and Y. Zhu and H. R. Guimãraes and N. Coallier and S. Maucourt and P. Giovenazzo and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105031477347?origin=resultslist},
doi = {10.1109/JSEN.2026.3666127},
issn = {1530437X (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Sensors Journal},
volume = {26},
number = {8},
pages = {12157–12167},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Honey bees play a vital role in global ecosystems and agriculture through their pollination services. However, the Varroa destructor mite remains one of the most damaging parasites affecting honey bee health, contributing significantly to colony decline. Early detection of Varroa infestation is essential for effective beekeeping management. In this study, we propose a machine learning approach for predicting Varroa mite infestation levels based on the acoustic analysis of hive recordings. We introduce a set of nine spectral shape descriptors (SSDs) computed from conventional and modulation spectrograms to characterize the acoustic environment within hives. We evaluate the predictive power of these features against traditional cepstral features across multiple classifiers, including support vector machines (SVMs), random forests (RFs), and k-nearest neighbors (KNNs). Our results demonstrate that the proposed SSDs significantly improve the classification performance relative to conventional features across numerous figures of merit, particularly in hive-independent test settings. This work highlights the potential of spectral acoustic monitoring combined with the supervised learning as a scalable and noninvasive tool for precision beekeeping and early detection of parasitic threats. © 2001-2012 IEEE.},
keywords = {Acoustic measuring instruments, Acoustic monitoring, Acoustic variables measurement, Acoustics, Beehive acoustic, Beehive acoustics, Ecosystems, Food products, Honey bee, honey bees, Honeybee colonies, Learning systems, Machine learning approaches, Modulation spectrogram, Motion compensation, Nearest neighbor search, Parasite-, Random forests, Sanitary sewers, Spectrograms, Spectrographs, Support vector machines, Varroa destructor, Varroa mite infestation},
pubstate = {published},
tppubtype = {article}
}
Honey bees play a vital role in global ecosystems and agriculture through their pollination services. However, the Varroa destructor mite remains one of the most damaging parasites affecting honey bee health, contributing significantly to colony decline. Early detection of Varroa infestation is essential for effective beekeeping management. In this study, we propose a machine learning approach for predicting Varroa mite infestation levels based on the acoustic analysis of hive recordings. We introduce a set of nine spectral shape descriptors (SSDs) computed from conventional and modulation spectrograms to characterize the acoustic environment within hives. We evaluate the predictive power of these features against traditional cepstral features across multiple classifiers, including support vector machines (SVMs), random forests (RFs), and k-nearest neighbors (KNNs). Our results demonstrate that the proposed SSDs significantly improve the classification performance relative to conventional features across numerous figures of merit, particularly in hive-independent test settings. This work highlights the potential of spectral acoustic monitoring combined with the supervised learning as a scalable and noninvasive tool for precision beekeeping and early detection of parasitic threats. © 2001-2012 IEEE.
2.
Abdollahi, M.; Zhu, Y.; Guimaraes, H. R.; Coallier, N.; Maucourt, S.; Giovenazzo, P.; Falk, T. H.
Audio Modulation Spectral Features for Improved Honeybee Colony Population Prediction Article de journal
Dans: IEEE Sensors Journal, vol. 25, no 24, p. 44378–44391, 2025, ISSN: 1530437X (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Apis mellifera, Audio acoustics, Audio recordings, Beehive acoustic, Beehive acoustics, Biodiversity, Chemical contamination, Climate variation, Ecology, Food security, Food supply, Honeybee, Honeybee colonies, honeybees, Modulation spectrogram, Parasite-, Population statistics, Spectral feature, Spectrograms
@article{abdollahiAudioModulationSpectral2025,
title = {Audio Modulation Spectral Features for Improved Honeybee Colony Population Prediction},
author = {M. Abdollahi and Y. Zhu and H. R. Guimaraes and N. Coallier and S. Maucourt and P. Giovenazzo and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105020704883?origin=resultslist},
doi = {10.1109/JSEN.2025.3625178},
issn = {1530437X (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Sensors Journal},
volume = {25},
number = {24},
pages = {44378–44391},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Honeybees (Apis mellifera) are vital to agriculture and biodiversity, serving as primary pollinators for numerous crops and wild plants. However, the decline in bee populations due to factors such as pesticides, pathogens, parasites, and climate variations poses a serious threat to food security and ecological balance. This study introduces the use of modulation spectral features, extracted from beehive acoustic signals, to better predict the strength of the honeybee colony. Experiments conducted on the public urban beehive acoustics and phenotyping dataset (UrBAN), comprised of over 3000 h of beehive audio recordings, show the proposed features offering improved predictive performance compared with traditional audio features. This work underscores the potential of automated, noninvasive acoustic monitoring systems to support sustainable beekeeping and ecological preservation. © 2001-2012 IEEE.},
keywords = {Apis mellifera, Audio acoustics, Audio recordings, Beehive acoustic, Beehive acoustics, Biodiversity, Chemical contamination, Climate variation, Ecology, Food security, Food supply, Honeybee, Honeybee colonies, honeybees, Modulation spectrogram, Parasite-, Population statistics, Spectral feature, Spectrograms},
pubstate = {published},
tppubtype = {article}
}
Honeybees (Apis mellifera) are vital to agriculture and biodiversity, serving as primary pollinators for numerous crops and wild plants. However, the decline in bee populations due to factors such as pesticides, pathogens, parasites, and climate variations poses a serious threat to food security and ecological balance. This study introduces the use of modulation spectral features, extracted from beehive acoustic signals, to better predict the strength of the honeybee colony. Experiments conducted on the public urban beehive acoustics and phenotyping dataset (UrBAN), comprised of over 3000 h of beehive audio recordings, show the proposed features offering improved predictive performance compared with traditional audio features. This work underscores the potential of automated, noninvasive acoustic monitoring systems to support sustainable beekeeping and ecological preservation. © 2001-2012 IEEE.